Abstract
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
Highlights
IntroductionOne of the most critical aspects of context is the identification of the activity the user is engaged in; for instance, the needs of a user when she is sleeping are completely different from the ones of the same subject when is commuting
The use of context in modern computer applications is what differentiates them from older ones because the context makes it possible to give more flexibility so that the application adapts to the changing needs of users [1].One of the most critical aspects of context is the identification of the activity the user is engaged in; for instance, the needs of a user when she is sleeping are completely different from the ones of the same subject when is commuting
In the view of these considerations, in this work we have made a significant effort in identifying the main families of fusion methods for Human Activity Recognition (HAR) and we have developed a systematic comparison of them, which is the main substance of this survey
Summary
One of the most critical aspects of context is the identification of the activity the user is engaged in; for instance, the needs of a user when she is sleeping are completely different from the ones of the same subject when is commuting. This explains why the automated recognition of users’ activity has been an important research area in recent years [2]. Recognition of these activities can help deliver proactive and personalized services in different applications [3]. Kerr et al [49] present an approach to recognizing sedentary behavior
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